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Agent Skills Demo

This demo showcases the Agent Skills standard integration with ADK. It demonstrates how to:

  1. Load skills from directories using AgentSkillLoader
  2. Convert skills to ADK tools using SkillTool
  3. Generate discovery prompts for the LLM
  4. Use skills with progressive disclosure

Running the Demo

# From the adk-python root directory
adk web contributing/samples/agent_skills_demo

What's Included

Skills Loaded

The demo loads BigQuery ML skills from src/google/adk/tools/bigquery/skills/:

  • bqml - Machine learning in BigQuery
  • bq-ai-operator - AI operations (text generation, embeddings)

Skill Structure (Agent Skills Standard)

Each skill follows the Agent Skills standard format:

skill-name/
├── SKILL.md           # Short description and instructions
├── references/        # Detailed documentation
│   ├── MODEL_TYPES.md
│   └── BEST_PRACTICES.md
├── scripts/          # Helper scripts
│   └── validate_model.py
└── assets/           # Templates, configs

Progressive Disclosure

Skills support three stages:

  1. Discovery (Stage 1): Minimal metadata shown in discovery prompt
  2. Activation (Stage 2): Full instructions loaded on demand
  3. Execution (Stage 3): Scripts and references loaded as needed

Example Interactions

Discover Available Skills

User: What ML skills do you have?
Agent: [Reviews discovery prompt and lists available skills]

Activate a Skill

User: Tell me about BQML
Agent: [Activates bqml skill, provides detailed instructions]

Load Reference Documentation

User: What model types are available?
Agent: [Loads MODEL_TYPES.md reference, explains options]

Run a Script

User: Validate my model configuration
Agent: [Runs validate_model.py script]

Code Walkthrough

from google.adk.skills import AgentSkillLoader, SkillTool

# Load skills from directory
loader = AgentSkillLoader()
loader.add_skill_directory("./skills")

# Create tools for agent
skill_tools = [SkillTool(skill) for skill in loader.get_all_skills()]

# Generate system prompt
discovery_prompt = loader.generate_discovery_prompt()

# Create agent with skill tools
agent = LlmAgent(
    model="gemini-2.0-flash",
    instruction=f"Available skills:\n{discovery_prompt}",
    tools=skill_tools,
)

Customization

Adding Custom Skills

  1. Create a skill directory following the Agent Skills standard
  2. Add SKILL.md with YAML frontmatter
  3. Add references, scripts, and assets as needed
  4. Update the SKILLS_DIR path in agent.py

Skill Configuration

Skills can include ADK-specific configuration in their frontmatter:

adk:
  config:
    timeout_seconds: 300
    max_parallel_calls: 5
  allowed_callers:
    - my_agent

Learn More